To Find Product-Market Fit, Look Beyond the Market

A fledgling business will ultimately fail if it never discovers a pool of target customers whose needs have not yet been sufficiently met. To discover product-market fit, many entrepreneurs look at what competitors have been doing, or they ask potential customers what they want. My research has shown that more freely available, reliable data on product-market fit may be found in an unexpected place: people who are using early versions of a product, service, or technology that has not yet been commercialized. By studying these passionate “early adopters,” entrepreneurs may glean information that can be used to identify the potential market acceptance of a more complete version of the product.

Jon Eckhardt, executive director, Weinert Center for Entrepreneurship

In a study of more than 16,000 mobile apps over 36 months, my research showed that the presence of free mobile apps within a specific market category likely spurred sales among priced mobile apps introduced within the same category. This goes against the conventional assumption that free mobile apps would act as substitutes for its priced equivalents, thus reducing sales.

Customer-Centric Market Discovery
Customer-centric approaches toward finding product-market fit—such as the Lean Startup—say entrepreneurs should find out what customers want by working closely with them while developing the product, instead of building the product and then trying to find a market for it. Within this framework, a potential product feature is treated as a hypothesis about demand for that feature that is subsequently tested for market receptiveness. Product-market fit is discovered when the iterative process produces a product with features that an identifiable group of customers are willing to buy.

Within this framework one might ask: where do product hypotheses come from? Proponents of the Lean Startup approach recommend that entrepreneurs interview customers to identify product hypotheses to build and test. Customer interviews can be valuable because they can keep entrepreneurs focused on serving the needs of actual people. But interviews are often not a reliable means to develop robust inferences about the preferences of a population. Researchers and entrepreneurs know that hypotheses developed from customer interviews can be misleading in ways that are difficult to detect.

Using Data for Hypothesis Generation
Existing data, especially when combined with interview data, can be a powerful source of information that can be used to generate product hypotheses.

In my experience, market-focused entrepreneurs often overlook the full range of existing data available on user experiences when searching for product-market fit. As indicated by the table below, technologies and products that generate user data can be thought of as being made available under four different economic logics.

Commercial logics. Economic logics #1 and #3 are well known to most entrepreneurs as they represent technologies that are made available to users by commercial providers. Logic #1 refers to products and services that are made available for free as part of a for-profit business model, such as advertising-based businesses or try-before-you-buy pricing models. Logic #3 refers to for-profit business models where customers must pay to use a product or service.

Non-Commercial logics. Non-commercial actors, such as individual hobbyists, nonprofits, and associations, make available a wide range of offerings for free under economic logic #2, ranging from food to technology to services. For example, individuals at a potluck may notice that a particular dish, such as a pie, is popular and hence discover a recipe for a product that might be viable as a commercial product. Non-commercial actors also produce items for a fee under economic logic #4, such as academic research labs that sell biological materials to fund their continued research. These operations are often small and focused on cost recovery instead of becoming a business.

Economic logics #1 and #3 certainly generate useful public information about user preferences. However, it creates a fundamental problem for entrepreneurs using this information for hypothesis generation: the data shows a competitor has already discovered the market. This means that the entrepreneur is likely to be forced into trying to catch a market leader instead of starting as a market pioneer.

However, items that are distributed under non-commercial logics are often non-competitive and hence are likely to produce more valuable insights to shape the customer hypothesis. Non-commercial actors are often primarily interested in the technology itself or in finding solutions to their own individual needs, instead of meeting the needs of consumers. This means that they are unlikely to build specific features demanded by paying customers, or to invest in supporting structures such as customer service. This creates opportunities for the entrepreneur to take the product to that next level.

In addition, because many non-commercial actors avoid involvement in any form of business, they will often provide low-cost information to seed stage entrepreneurs. Non-commercial actors may be willing to sell their technologies or even partner with others if they are assured it won’t inhibit their ability to continue working on their passions. This provides further opportunities for those who wish to operate under the commercial logic.

In sum, information generated from both commercial and non-commercial actors can be utilized by seed stage entrepreneurs in finding a proper product-market fit. Because many seed stage entrepreneurs are often cash-strapped, the low-cost information provided by non-commercial sources may best help entrepreneurs to pose practical questions, identify important issues, and develop solutions prior to product release.

Jon Eckhardt is executive director of the Weinert Center for Entrepreneurship and the Robert Pricer Chair in Enterprise Development at the Wisconsin School of Business. He is also a cofounder of gener8tor, one of the top-ranked business accelerators in the United States. This research was funded in part by the Richard M. Schulze Family Foundation and the Ewing Marion Kauffman Foundation.